Machine Learning Fraud Detection Ensemble Strategy

Architect a multi-layered machine learning ensemble approach for real-time financial fraud detection that combines at least four different algorithmic strategies. Design a workflow that integrates anomaly detection, supervised classification, unsupervised clustering, and reinforcement learning models. Include a robust feature engineering pipeline that can handle high-dimensional transactional data with minimal information loss and provide explainable AI interpretations for each flagged suspicious activity.